Heart disease is the leading cause of death in the United States. A heart attack, also known as an acute myocardial infarction (AMI), typically results from a blood clot or “thrombus” that obstructs blood flow in one or more coronary arteries. AMI is a common and life-threatening complication of coronary artery disease. Coronary ischemia is caused by an insufficiency of oxygen to the heart muscle. Ischemia is typically provoked by physical activity or other causes of increased heart rate when one or more of the coronary arteries is narrowed by atherosclerosis. AMI, which is typically the result of a completely blocked coronary artery, is the most extreme form of ischemia. Patients will often (but not always) become aware of chest discomfort, known as “angina”, when the heart muscle is experiencing ischemia. Those with coronary atherosclerosis are at higher risk for AMI if the plaque becomes further obstructed by thrombus.
Detection of AMI often involves analyzing changes in a person's ST segment voltage. A common scheme for computing changes in the ST segment involves determining a quantity known as ST deviation for each beat. ST deviation is the value of the electrocardiogram at a point or points during the ST segment relative to the value of the electrocardiogram at some point or points during the PQ segment. A variety of schemes have been proposed to automatically determine both ST points and PQ points.
U.S. Pat. No. 6,397,100 discloses a method for detecting the isoelectric point (PQ point) for the QRST complex, by searching backwards from the R wave peak for a locally minimum slope.
Hayn et al., (“Automated QT Interval Measurement from Multilead ECG Signals”, Comp. Card. 33:381-4, 2006) describe a method for detecting Q wave onset by first selecting a coarse QRS onset point by comparing the change in the signal over time (i.e. gradient) to a threshold. This coarse onset point is then fine tuned by a stepwise decrease of the threshold. For each such step, a “possible onset point was calculated and the mean range curve value right before and after this possible point was determined. The point with the lowest ratio in between these mean values was chosen as the exact onset point.” The “range curve” is the difference in signal amplitude, so a ratio of range curves before and after a point is a ratio of first derivative/gradient quantities.
Sun et al. (“Characteristic wave detection in ECG signal using morphological transform”; BMC Cardiovasc Disord. 2005; 5: 28) describe a multi-scale derivative method for locating QRS (and P and T) wave fiducial points. “Long distance” derivatives/differences (e.g. f(x+n)−f(x), where n>1) are taken both before and after each candidate onset point and the difference between these derivatives is calculated to form a type of second derivative/difference which the authors term a “multiscale morphological derivative transform.” QRS onset or other fiducial points are defined as the maxima or minima of this “transform.” Kemmelings et. al. describe a QRS onset/offset detection scheme that involves summing the absolute value of the first derivative (difference) and then taking a “long distance” derivative of this summed signal, to find where it abruptly changes (over a large scale). (“Automatic QRS onset and offset detection for body surface Q RS integral mapping of ventricular tachycardia.”; IEEE Trans Biomed Eng. 1994; 41:830-836).
Zong et al. (“A robust open-source algorithm to detect onset and duration of QRS complexes”, Computers in Cardiology, 2003, Issue, 21-24 Sep. 2003, Page(s): 737-740) describe a method for detecting QRS onset and offset points by applying thresholds to the values of a function that corresponds to the “distance” along the signal; the function has the form (D2+(Δs/Δt)2)0.5, where D is a constant and Δs is the difference between successive samples of an ECG signal and Δt is the time between samples (i.e. the inverse of the sampling frequency). This function is essentially a discrete version of the calculus formula for distance along a curve, which is based on the first derivative of the curve.
U.S. Pat. No. 5,758,654 describes a method for QRS onset/offset detection. First, a QRS peak is located based upon, among other things, second derivative criteria. Adjacent QRS extrema (i.e. peaks) are located, apparently by stepping through consecutive waveform samples, and selected as coarse QRS onset/offset times. The coarse onset/offset times are subsequently refined based on criteria related to the region of the waveform bounded by optimal QRS onset/offset points.
U.S. Pat. No. 6,650,931 to McClure et al. describes search techniques for finding the onsets and offsets of various cardiac events. To detect QRS offset points, a search is performed within a predefined search window. One criterion for a valid QRS offset point is that “the absolute value of the summation of the differences between a given number of consecutive pairs of sample points occurring .Δt msec apart” must be less than a pre-defined value. Also, “the maximum amplitude of all the signal sample points used in satisfying [the above criterion] must be less than a pre-defined maximum amplitude.”
U.S. Pat. No. 6,625,490 to McClure et al. describes a “System and method of automatically adjusting sensing parameters based on temporal measurement of cardiac events.”
Laguna et al. (Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database Source Computers and Biomedical Research, Volume 27, Issue 1 (February 1994), describe a method for detecting QRS offset by applying a slope test. The QRS maximum is located, and the ST point is chosen as the point after that at which the slope decreases to a specified fraction of the maximum slope. If no such sample is found, then the minimum value of the slope is taken as the ST point.
Despite all of the foregoing work that has been done, there is still a need for an effective system for detecting QRS onset and offset points.